Beijing–Tianjin–Hebei is an important agricultural production area in China, and farmers’ agrochemical input behavior directly affects the risk of agricultural non-point source pollution and the effect of green agricultural development. Based on a questionnaire survey and field interview data, this study investigated the agrochemical input behavior of farmers in Beijing–Tianjin–Hebei, and analyzed its influencing factors. Using the Probit model, we carried out an empirical study on farmers’ willingness to invest in cleaner production of agrochemicals from four aspects: farmers’ characteristics, agricultural input, environmental awareness and technical cognition. The results showed that the kinds of fertilizer were mainly compound fertilizer, urea and organic fertilizer, and the fertilization method was mainly surface spreading, accounting for 50.6% of the total surveys; the number of agrochemicals was determined chiefly by agricultural sellers, accounting for 55.5%. The proportion of the guidance from technical departments in Beijing was higher than that of Tianjin and Hebei. The first influencing factor for farmers’ behavior towards agrochemical input was the pursuit of high yield and high profit, accounting for 24.9%, 22.6% and 26.0%, respectively. The guidance of relevant technical departments still did not fully cover the use of agrochemicals. The study also found that factors such as the price of farming materials, the price of agricultural products, family income, farmland facilities, government propaganda, technical training and subsidies all impacted the agrochemical input behavior. Pre-production technical guidance and farmers’ awareness significantly affected the willingness to adopt cleaner production. Technical training was helpful to improve farmers’ willingness to participate actively, and enhancing the pertinence of training played an important role in the adoption of cleaner production technology. In conclusion, the influencing factors of farmers’ agrochemical input in Beijing, Tianjin and Hebei were complex, and the scientific application level still needs to be improved. This paper finally discusses and puts forward some countermeasures and suggestions for agrochemical reduction and efficiency improvement.

Enhancing agricultural green total factor productivity (AGTFP) under ecological and environmental constraints is essential for advancing green agricultural development in the Beijing–Tianjin–Hebei (BTH) region. Using panel data from 13 prefecture-level cities from 2001 to 2022, this study applies a super-efficiency EBM model incorporating undesirable outputs together with the Malmquist–Luenberger index to measure AGTFP. Global and local Moran’s I indices as well as the spatial Durbin model are then employed to examine the temporal evolution, spatial disparities, and spatial interaction effects of AGTFP during 2001–2022. The findings indicate that: (1) From 2001 to 2022, the AGTFP in the BTH region grew at an average annual rate of 7.7%. This trend reflects a growth pattern primarily driven by green technological progress in agriculture, while substantial disparities in AGTFP persist across different subregions. (2) the global Moran’s I values show frequent shifts between positive and negative spatial autocorrelation, suggesting that a stable and effective regional coordination mechanism for green agricultural development has yet to be formed; (3) the determinants of AGTFP exhibit pronounced spatiotemporal heterogeneity, and the fundamental drivers of the region’s green agricultural transition increasingly rely on endogenous growth generated by technological innovation and rural human capital; (4) policy recommendations include strengthening benefit-sharing and policy coordination mechanisms, promoting cross-regional cooperation in agricultural science and technology, and implementing differentiated industrial layouts to support green agricultural development in the BTH region. These results provide valuable insights for promoting coordinated and sustainable green agricultural development across regions.

Making clear the exact amount of carbon emissions from the planting industry is of great significance for developing low-carbon agriculture and helping achieve carbon neutrality. The current carbon emissions from the planting industry in Beijing, the capital of China, are still unclear, and there is a lack of quantitative research on the production, economic, and ecological benefits of carbon emissions. This paper used the carbon emissions factor method to study the inter-annual variation characteristics of carbon emissions and carbon benefits in Beijing’s planting industry since 2000. The results show that the carbon emissions from the planting industry in Beijing in 2023 were 256,400 tons, of which the carbon emissions from agricultural inputs, nitrous oxide (N₂O) from farmland, and methane (CH₄) from rice cultivation were 149,300, 105,200, and 2000 tons, respectively. From 2000 to 2023, the total carbon emissions from the planting industry in Beijing have shown a downward trend. Compared with 2000, the carbon emissions from agricultural inputs and N₂O in 2023 decreased by 59.88% and 74.52%, respectively. The carbon emissions of CH₄ from rice cultivation were only 2.38% of those in 2000, and the total carbon emissions from the planting industry in Beijing decreased by 70.43%. The average carbon emissions from agricultural inputs and N₂O accounted for 50.85% and 47.95% of the total level of the planting industry, respectively, and were the main sources of carbon emissions in Beijing. Chemical fertilizer and agricultural film inputs were important sources of carbon emissions from agricultural inputs. Reducing inputs for agriculture and sources of N₂O from farmland is an important way to reduce carbon emissions from agriculture in Beijing. In the end, some suggestions were proposed for reducing carbon emissions from the planting industry.

Employment promotion and employment realization are the core and fundamental problems in the resettlement of land-expropriated farmers transferred to citizens. To solve this problem, it is necessary to clarify the key factors and mechanisms that affect the employment behavior of “farmers to citizens” workers. Taking the labor force from land-expropriated “farmers to citizens” in the construction of Beijing city sub-center as the research object, this paper utilizes Logistic ISM to determine the key factors affecting the employment behavior of the labor force when changing from rural to urban, as well as the internal logical relationship and hierarchical structure among the influencing factors. The results show that only 40% of the migrant workers in the sample have achieved employment, while 69% of the unemployed population have a willingness to work but are limited by age, skills, and family factors. The logistic regression model identifies that the employment behavior of land-expropriated farmers is significantly affected by 10 factors, including gender, age, work experience, hobbies, employment demand, expenditure change, employment difficulty cognition, government training, policy satisfaction and social security. Among them, ISM further reveals that these factors form a three-level hierarchical mechanism of “structure–cognition–behavior”; gender, social security and policy satisfaction are the deep-root factors, and the intermediate factors, such as hobbies and government training, affect employment demand, employment difficulty cognition and other surface factors, and ultimately affect the employment behavior of land-expropriated “farmers to citizens”. Based on this, it is proposed to start from four aspects: differentiated employment guidance, policy transmission optimization, service efficiency improvement, and industrial driving, to systematically promote the realization of more comprehensive and stable employment for the rural-to-residential population, and provide institutional guarantees and practical paths for their sustainable livelihoods.